In order to realize the safety state monitoring and health management of the aero-engine fuel system

the fuel system performance degradation detection and remaining useful life estimation was researched.Taking the fuel system fuel metering device as an example

the main performance degradation mode was analyzed.The health indicators (HIs) selection scheme based on current-speed data was designed.Considering the uncertainty of environment and model parameters

the model uncertainty simulation was carried out.The component performance degradation was detected based on the Mahalanobis distance between the healthy data and the performance degradative data.A remaining useful life (RUL) estimation method based on random forest-support vector regression (RF-SVR) was proposed.The component RUL estimation was realized by SVR model optimized by RF feature selection.Finally

the method was validated based on the simulation data of a certain type of civil turbofan engine mechanical hydraulic model.The results show that the performance of the method has a false alarm rate and a false negative rate of less than 2%

and the RUL estimation error is less than 3%. This provides a reference for predictive maintenance of aero engine fuel systems.